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APM in the API Economy - Part 2

Julie Craig

The following is an edited excerpt from Application Performance Management (APM) in the Digital Enterprise: Managing Applications for Cloud, Mobile, IT, and eBusinessby Rick Sturm (CEO, Enterprise Management Associates), Carol Pollard and Julie Craig (Research Director for Applications, Enterprise Management Associates). The book is available now from Amazon.

This blog condenses some of the key concepts covered in Chapter 11, entitled Application Programming Interfaces and Connected Systems.

Start with APM in the API Economy - Part 1

API Management Tools

So how and where do tools fit into this picture? Tools help rein in this growth and complexity by addressing key functional questions supporting tools acquisitions.

API providers often find themselves asking:

■ How can we track usage growth and the impact of that growth on back-end systems for capacity planning purposes?

■ How do we ensure that only authorized users and applications connect to our systems?

■ How can our organization synchronize API development with traditional application development lifecycles since the two are often linked?

■ How can we secure API usage to ensure that sensitive data is protected?

■ How can we track usage of “for pay” services to correctly bill for access?

API consumers ask:

■ How do we find out about new APIs offered by our vendors and partners, and how do we then go about accessing them?

■ How do we know when the APIs our systems are accessing are modified by the provider?

■ We have hundreds of applications that access APIs—and some of them interact with one another. How do we measure end-to-end performance? And when one such application fails, how can we determine what changed, what's wrong, and how to fix it?

A large majority of both consumers and providers are monitoring performance and availability of applications accessing APIs from the perspective of the gateway. Although this is a good starting point, it is essentially a silo solution to a far broader and more complex problem.

API-connected applications, like any other tiered, distributed, or componentized applications, may have hundreds or thousands of potential failure points. From this perspective, simply monitoring the gateway is akin to monitoring server, network, or database silos. Such monitoring fails to address the touch points BETWEEN hardware and software elements that occur during application execution. In other words, it lacks the visibility to the entire end-to-end execution path that distinguishes application management from systems or silo management.

In the end, APM platforms — and API management systems -- should have mechanisms for incorporating gateway performance data into analytics, correlations, and dashboards. A few vendors are already addressing the API market with data-sharing capabilities and/or and products specifically designed to manage API-connected applications across each stage of the lifecycle. Lacking a single point of visibility and control to application execution (versus silo performance), full automation of the end-to-end monitoring/management function remains a fruitless quest.

EMA is currently in the process of launching new research into the automation and tools supporting the API Economy. This study, “Enterprise Management Strategies for the Connected Business: Hybrid Services and API Ecosystems Become Business as Usual”, will include both a user-facing survey and vendor “snapshots” encapsulating the types of tools and capabilities currently available in the enterprise management tools market.

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APM in the API Economy - Part 2

Julie Craig

The following is an edited excerpt from Application Performance Management (APM) in the Digital Enterprise: Managing Applications for Cloud, Mobile, IT, and eBusinessby Rick Sturm (CEO, Enterprise Management Associates), Carol Pollard and Julie Craig (Research Director for Applications, Enterprise Management Associates). The book is available now from Amazon.

This blog condenses some of the key concepts covered in Chapter 11, entitled Application Programming Interfaces and Connected Systems.

Start with APM in the API Economy - Part 1

API Management Tools

So how and where do tools fit into this picture? Tools help rein in this growth and complexity by addressing key functional questions supporting tools acquisitions.

API providers often find themselves asking:

■ How can we track usage growth and the impact of that growth on back-end systems for capacity planning purposes?

■ How do we ensure that only authorized users and applications connect to our systems?

■ How can our organization synchronize API development with traditional application development lifecycles since the two are often linked?

■ How can we secure API usage to ensure that sensitive data is protected?

■ How can we track usage of “for pay” services to correctly bill for access?

API consumers ask:

■ How do we find out about new APIs offered by our vendors and partners, and how do we then go about accessing them?

■ How do we know when the APIs our systems are accessing are modified by the provider?

■ We have hundreds of applications that access APIs—and some of them interact with one another. How do we measure end-to-end performance? And when one such application fails, how can we determine what changed, what's wrong, and how to fix it?

A large majority of both consumers and providers are monitoring performance and availability of applications accessing APIs from the perspective of the gateway. Although this is a good starting point, it is essentially a silo solution to a far broader and more complex problem.

API-connected applications, like any other tiered, distributed, or componentized applications, may have hundreds or thousands of potential failure points. From this perspective, simply monitoring the gateway is akin to monitoring server, network, or database silos. Such monitoring fails to address the touch points BETWEEN hardware and software elements that occur during application execution. In other words, it lacks the visibility to the entire end-to-end execution path that distinguishes application management from systems or silo management.

In the end, APM platforms — and API management systems -- should have mechanisms for incorporating gateway performance data into analytics, correlations, and dashboards. A few vendors are already addressing the API market with data-sharing capabilities and/or and products specifically designed to manage API-connected applications across each stage of the lifecycle. Lacking a single point of visibility and control to application execution (versus silo performance), full automation of the end-to-end monitoring/management function remains a fruitless quest.

EMA is currently in the process of launching new research into the automation and tools supporting the API Economy. This study, “Enterprise Management Strategies for the Connected Business: Hybrid Services and API Ecosystems Become Business as Usual”, will include both a user-facing survey and vendor “snapshots” encapsulating the types of tools and capabilities currently available in the enterprise management tools market.

Hot Topics

The Latest

The enterprises that will define the next decade are not the ones that deployed the most technology. They are the ones who understood what their technology was actually doing. That distinction is not a philosophical point. It is the central operational challenge facing every organization that has spent the last five years modernizing at speed ...

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...